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1. SPhyR: tumor phylogeny estimation from single-cell sequencing data under loss and error

   https://doi.org/10.1093/bioinformatics/bty589  

   El-Kebir, Mohammed ( September 2018 , Bioinformatics)

   Cancer is characterized by intra-tumor heterogeneity, the presence of distinct cell populations with distinct complements of somatic mutations, which include single-nucleotide variants (SNVs) and copy-number aberrations (CNAs). Single-cell sequencing technology enables one to study these cell populations at single-cell resolution. Phylogeny estimation algorithms that employ appropriate evolutionary models are key to understanding the evolutionary mechanisms behind intra-tumor heterogeneity.

   We introduce Single-cell Phylogeny Reconstruction (SPhyR), a method for tumor phylogeny estimation from single-cell sequencing data. In light of frequent loss of SNVs due to CNAs in cancer, SPhyR employs the k-Dollo evolutionary model, where a mutation can only be gained once but lost k times. Underlying SPhyR is a novel combinatorial characterization of solutions as constrained integer matrix completions, based on a connection to the cladistic multi-state perfect phylogeny problem. SPhyR outperforms existing methods on simulated data and on a metastatic colorectal cancer.

   SPhyR is available on https://github.com/elkebir-group/SPhyR.

   Supplementary data are available at Bioinformatics online.

    

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2. Phertilizer: Growing a clonal tree from ultra-low coverage single-cell DNA sequencing of tumors

   https://doi.org/10.1371/journal.pcbi.1011544  

   Weber, Leah L. ; Zhang, Chuanyi ; Ochoa, Idoia ; El-Kebir, Mohammed ( October 2023 , PLOS Computational Biology)

   Przytycka, Teresa M. (Ed.)

   Emerging ultra-low coverage single-cell DNA sequencing (scDNA-seq) technologies have enabled high resolution evolutionary studies of copy number aberrations (CNAs) within tumors. While these sequencing technologies are well suited for identifying CNAs due to the uniformity of sequencing coverage, the sparsity of coverage poses challenges for the study of single-nucleotide variants (SNVs). In order to maximize the utility of increasingly available ultra-low coverage scDNA-seq data and obtain a comprehensive understanding of tumor evolution, it is important to also analyze the evolution of SNVs from the same set of tumor cells. We presentPhertilizer, a method to infer a clonal tree from ultra-low coverage scDNA-seq data of a tumor. Based on a probabilistic model, our method recursively partitions the data by identifying key evolutionary events in the history of the tumor. We demonstrate the performance ofPhertilizeron simulated data as well as on two real datasets, finding thatPhertilizereffectively utilizes the copy-number signal inherent in the data to more accurately uncover clonal structure and genotypes compared to previous methods.

    

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3. CNAsim: improved simulation of single-cell copy number profiles and DNA-seq data from tumors

   https://doi.org/10.1093/bioinformatics/btad434  

   Weiner, Samson ; Bansal, Mukul S. ; Kendziorski, ed., Christina ( July 2023 , Bioinformatics)

   CNAsim is a software package for improved simulation of single-cell copy number alteration (CNA) data from tumors. CNAsim can be used to efficiently generate single-cell copy number profiles for thousands of simulated tumor cells under a more realistic error model and a broader range of possible CNA mechanisms compared with existing simulators. The error model implemented in CNAsim accounts for the specific biases of single-cell sequencing that leads to read count fluctuation and poor resolution of CNA detection. For improved realism over existing simulators, CNAsim can (i) generate WGD, whole-chromosomal CNAs, and chromosome-arm CNAs, (ii) simulate subclonal population structure defined by the accumulation of chromosomal CNAs, and (iii) dilute the sampled cell population with both normal diploid cells and pseudo-diploid cells. The software can also generate DNA-seq data for sampled cells.

   CNAsim is written in Python and is freely available open-source from https://github.com/samsonweiner/CNAsim.

    

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4. Phylovar: toward scalable phylogeny-aware inference of single-nucleotide variations from single-cell DNA sequencing data

   https://doi.org/10.1093/bioinformatics/btac254  

   Edrisi, Mohammadamin ; Valecha, Monica V. ; Chowdary, Sunkara B. V. ; Robledo, Sergio ; Ogilvie, Huw A. ; Posada, David ; Zafar, Hamim ; Nakhleh, Luay ( June 2022 , Bioinformatics)

   Single-nucleotide variants (SNVs) are the most common variations in the human genome. Recently developed methods for SNV detection from single-cell DNA sequencing data, such as SCIΦ and scVILP, leverage the evolutionary history of the cells to overcome the technical errors associated with single-cell sequencing protocols. Despite being accurate, these methods are not scalable to the extensive genomic breadth of single-cell whole-genome (scWGS) and whole-exome sequencing (scWES) data.

   Here, we report on a new scalable method, Phylovar, which extends the phylogeny-guided variant calling approach to sequencing datasets containing millions of loci. Through benchmarking on simulated datasets under different settings, we show that, Phylovar outperforms SCIΦ in terms of running time while being more accurate than Monovar (which is not phylogeny-aware) in terms of SNV detection. Furthermore, we applied Phylovar to two real biological datasets: an scWES triple-negative breast cancer data consisting of 32 cells and 3375 loci as well as an scWGS data of neuron cells from a normal human brain containing 16 cells and approximately 2.5 million loci. For the cancer data, Phylovar detected somatic SNVs with high or moderate functional impact that were also supported by bulk sequencing dataset and for the neuron dataset, Phylovar identified 5745 SNVs with non-synonymous effects some of which were associated with neurodegenerative diseases.

   Phylovar is implemented in Python and is publicly available at https://github.com/NakhlehLab/Phylovar.

    

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5. Parsimonious Clone Tree Integration in cancer

   https://doi.org/10.1186/s13015-022-00209-9  

   Sashittal, Palash ; Zaccaria, Simone ; El-Kebir, Mohammed ( December 2022 , Algorithms for Molecular Biology)

   Abstract Background Every tumor is composed of heterogeneous clones, each corresponding to a distinct subpopulation of cells that accumulated different types of somatic mutations, ranging from single-nucleotide variants (SNVs) to copy-number aberrations (CNAs). As the analysis of this intra-tumor heterogeneity has important clinical applications, several computational methods have been introduced to identify clones from DNA sequencing data. However, due to technological and methodological limitations, current analyses are restricted to identifying tumor clones only based on either SNVs or CNAs, preventing a comprehensive characterization of a tumor’s clonal composition. Results To overcome these challenges, we formulate the identification of clones in terms of both SNVs and CNAs as a integration problem while accounting for uncertainty in the input SNV and CNA proportions. We thus characterize the computational complexity of this problem and we introduce PACTION (PArsimonious Clone Tree integratION), an algorithm that solves the problem using a mixed integer linear programming formulation. On simulated data, we show that tumor clones can be identified reliably, especially when further taking into account the ancestral relationships that can be inferred from the input SNVs and CNAs. On 49 tumor samples from 10 prostate cancer patients, our integration approach provides a higher resolution view of tumor evolution than previous studies. Conclusion PACTION is an accurate and fast method that reconstructs clonal architecture of cancer tumors by integrating SNV and CNA clones inferred using existing methods. 

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